Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations8000
Missing cells5848
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory243.2 B

Variable types

Numeric6
Text1
Categorical6

Alerts

Surname has 409 (5.1%) missing valuesMissing
CreditScore has 1641 (20.5%) missing valuesMissing
Balance has 1615 (20.2%) missing valuesMissing
NumOfProducts has 976 (12.2%) missing valuesMissing
HasCrCard has 375 (4.7%) missing valuesMissing
EstimatedSalary has 832 (10.4%) missing valuesMissing
CustomerId has unique valuesUnique
Tenure has 334 (4.2%) zerosZeros
Balance has 2283 (28.5%) zerosZeros

Reproduction

Analysis started2025-11-19 21:17:17.252914
Analysis finished2025-11-19 21:17:29.566078
Duration12.31 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

CustomerId
Real number (ℝ)

Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15691188
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-19T22:17:29.767680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15579208
Q115628960
median15691115
Q315753728
95-th percentile15802585
Maximum15815690
Range249989
Interquartile range (IQR)124768

Descriptive statistics

Standard deviation71872.267
Coefficient of variation (CV)0.0045804221
Kurtosis-1.1991968
Mean15691188
Median Absolute Deviation (MAD)62273
Skewness-0.0013115379
Sum1.2552951 × 1011
Variance5.1656227 × 109
MonotonicityNot monotonic
2025-11-19T22:17:30.088207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158112611
 
< 0.1%
155674311
 
< 0.1%
157047951
 
< 0.1%
155927731
 
< 0.1%
156862191
 
< 0.1%
157637471
 
< 0.1%
156479751
 
< 0.1%
157349701
 
< 0.1%
156652831
 
< 0.1%
156078271
 
< 0.1%
Other values (7990)7990
99.9%
ValueCountFrequency (%)
155657011
< 0.1%
155657061
< 0.1%
155657961
< 0.1%
155658061
< 0.1%
155658791
< 0.1%
155658911
< 0.1%
155659961
< 0.1%
155660911
< 0.1%
155661111
< 0.1%
155661391
< 0.1%
ValueCountFrequency (%)
158156901
< 0.1%
158156601
< 0.1%
158156561
< 0.1%
158156281
< 0.1%
158156261
< 0.1%
158155601
< 0.1%
158155301
< 0.1%
158154901
< 0.1%
158154431
< 0.1%
158154281
< 0.1%

Surname
Text

Missing 

Distinct2523
Distinct (%)33.2%
Missing409
Missing (%)5.1%
Memory size423.8 KiB
2025-11-19T22:17:30.706364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.4327493
Min length2

Characters and Unicode

Total characters48831
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1338 ?
Unique (%)17.6%

Sample

1st rowBoone
2nd rowShe
3rd rowKerr
4th rowLoggia
5th rowChiekwugo
ValueCountFrequency (%)
scott25
 
0.3%
lo25
 
0.3%
brown23
 
0.3%
martin23
 
0.3%
smith23
 
0.3%
yeh22
 
0.3%
shih21
 
0.3%
maclean18
 
0.2%
johnson18
 
0.2%
genovese18
 
0.2%
Other values (2520)7415
97.2%
2025-11-19T22:17:31.838078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a4409
 
9.0%
e4346
 
8.9%
n3978
 
8.1%
o3745
 
7.7%
i3398
 
7.0%
r2737
 
5.6%
l2185
 
4.5%
s1959
 
4.0%
u1913
 
3.9%
h1646
 
3.4%
Other values (45)18515
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)48831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a4409
 
9.0%
e4346
 
8.9%
n3978
 
8.1%
o3745
 
7.7%
i3398
 
7.0%
r2737
 
5.6%
l2185
 
4.5%
s1959
 
4.0%
u1913
 
3.9%
h1646
 
3.4%
Other values (45)18515
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a4409
 
9.0%
e4346
 
8.9%
n3978
 
8.1%
o3745
 
7.7%
i3398
 
7.0%
r2737
 
5.6%
l2185
 
4.5%
s1959
 
4.0%
u1913
 
3.9%
h1646
 
3.4%
Other values (45)18515
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a4409
 
9.0%
e4346
 
8.9%
n3978
 
8.1%
o3745
 
7.7%
i3398
 
7.0%
r2737
 
5.6%
l2185
 
4.5%
s1959
 
4.0%
u1913
 
3.9%
h1646
 
3.4%
Other values (45)18515
37.9%

CreditScore
Real number (ℝ)

Missing 

Distinct448
Distinct (%)7.0%
Missing1641
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean661.23526
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-19T22:17:32.453206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile500
Q1595
median664
Q3729
95-th percentile824
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation95.876126
Coefficient of variation (CV)0.14499548
Kurtosis-0.39748144
Mean661.23526
Median Absolute Deviation (MAD)67
Skewness-0.1203844
Sum4204795
Variance9192.2315
MonotonicityNot monotonic
2025-11-19T22:17:32.823035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850187
 
2.3%
70542
 
0.5%
67842
 
0.5%
68440
 
0.5%
66739
 
0.5%
68738
 
0.5%
65536
 
0.4%
68634
 
0.4%
65234
 
0.4%
63333
 
0.4%
Other values (438)5834
72.9%
(Missing)1641
 
20.5%
ValueCountFrequency (%)
3502
< 0.1%
3511
< 0.1%
3581
< 0.1%
3631
< 0.1%
3651
< 0.1%
3671
< 0.1%
3731
< 0.1%
3762
< 0.1%
3831
< 0.1%
4011
< 0.1%
ValueCountFrequency (%)
850187
2.3%
8497
 
0.1%
8481
 
< 0.1%
8474
 
0.1%
8465
 
0.1%
8454
 
0.1%
8446
 
0.1%
8432
 
< 0.1%
8426
 
0.1%
84110
 
0.1%

Geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size429.8 KiB
France
4030 
Germany
1986 
Spain
1984 

Length

Max length7
Median length6
Mean length6.00025
Min length5

Characters and Unicode

Total characters48002
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowFrance
3rd rowSpain
4th rowGermany
5th rowFrance

Common Values

ValueCountFrequency (%)
France4030
50.4%
Germany1986
24.8%
Spain1984
24.8%

Length

2025-11-19T22:17:33.044400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T22:17:33.188140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
france4030
50.4%
germany1986
24.8%
spain1984
24.8%

Most occurring characters

ValueCountFrequency (%)
n8000
16.7%
a8000
16.7%
r6016
12.5%
e6016
12.5%
F4030
8.4%
c4030
8.4%
G1986
 
4.1%
m1986
 
4.1%
y1986
 
4.1%
S1984
 
4.1%
Other values (2)3968
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)48002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n8000
16.7%
a8000
16.7%
r6016
12.5%
e6016
12.5%
F4030
8.4%
c4030
8.4%
G1986
 
4.1%
m1986
 
4.1%
y1986
 
4.1%
S1984
 
4.1%
Other values (2)3968
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n8000
16.7%
a8000
16.7%
r6016
12.5%
e6016
12.5%
F4030
8.4%
c4030
8.4%
G1986
 
4.1%
m1986
 
4.1%
y1986
 
4.1%
S1984
 
4.1%
Other values (2)3968
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n8000
16.7%
a8000
16.7%
r6016
12.5%
e6016
12.5%
F4030
8.4%
c4030
8.4%
G1986
 
4.1%
m1986
 
4.1%
y1986
 
4.1%
S1984
 
4.1%
Other values (2)3968
8.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size421.3 KiB
Male
4384 
Female
3616 

Length

Max length6
Median length4
Mean length4.904
Min length4

Characters and Unicode

Total characters39232
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male4384
54.8%
Female3616
45.2%

Length

2025-11-19T22:17:33.337784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T22:17:33.449457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male4384
54.8%
female3616
45.2%

Most occurring characters

ValueCountFrequency (%)
e11616
29.6%
a8000
20.4%
l8000
20.4%
M4384
 
11.2%
F3616
 
9.2%
m3616
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)39232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e11616
29.6%
a8000
20.4%
l8000
20.4%
M4384
 
11.2%
F3616
 
9.2%
m3616
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e11616
29.6%
a8000
20.4%
l8000
20.4%
M4384
 
11.2%
F3616
 
9.2%
m3616
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e11616
29.6%
a8000
20.4%
l8000
20.4%
M4384
 
11.2%
F3616
 
9.2%
m3616
 
9.2%

Age
Real number (ℝ)

Distinct69
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.935
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-19T22:17:33.586690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.381389
Coefficient of variation (CV)0.26663386
Kurtosis1.3939202
Mean38.935
Median Absolute Deviation (MAD)6
Skewness1.0060536
Sum311480
Variance107.77325
MonotonicityNot monotonic
2025-11-19T22:17:33.817204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35395
 
4.9%
37388
 
4.9%
38386
 
4.8%
34363
 
4.5%
33360
 
4.5%
36357
 
4.5%
40344
 
4.3%
39343
 
4.3%
32336
 
4.2%
31303
 
3.8%
Other values (59)4425
55.3%
ValueCountFrequency (%)
1815
 
0.2%
1921
 
0.3%
2031
 
0.4%
2143
 
0.5%
2262
 
0.8%
2374
0.9%
24107
1.3%
25109
1.4%
26164
2.1%
27166
2.1%
ValueCountFrequency (%)
921
 
< 0.1%
881
 
< 0.1%
841
 
< 0.1%
831
 
< 0.1%
821
 
< 0.1%
813
 
< 0.1%
803
 
< 0.1%
793
 
< 0.1%
785
0.1%
7710
0.1%

Tenure
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.013875
Minimum0
Maximum10
Zeros334
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-19T22:17:34.177752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8888104
Coefficient of variation (CV)0.57616323
Kurtosis-1.1575913
Mean5.013875
Median Absolute Deviation (MAD)2
Skewness0.0064856405
Sum40111
Variance8.3452256
MonotonicityNot monotonic
2025-11-19T22:17:34.437869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8837
10.5%
1832
10.4%
2822
10.3%
5819
10.2%
7813
10.2%
4810
10.1%
3790
9.9%
6779
9.7%
9771
9.6%
10393
4.9%
ValueCountFrequency (%)
0334
 
4.2%
1832
10.4%
2822
10.3%
3790
9.9%
4810
10.1%
5819
10.2%
6779
9.7%
7813
10.2%
8837
10.5%
9771
9.6%
ValueCountFrequency (%)
10393
4.9%
9771
9.6%
8837
10.5%
7813
10.2%
6779
9.7%
5819
10.2%
4810
10.1%
3790
9.9%
2822
10.3%
1832
10.4%

Balance
Real number (ℝ)

Missing  Zeros 

Distinct4103
Distinct (%)64.3%
Missing1615
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean76929.669
Minimum0
Maximum250898.09
Zeros2283
Zeros (%)28.5%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-19T22:17:34.694655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median98009.13
Q3127660.46
95-th percentile162851.45
Maximum250898.09
Range250898.09
Interquartile range (IQR)127660.46

Descriptive statistics

Standard deviation62292.133
Coefficient of variation (CV)0.80972834
Kurtosis-1.4803442
Mean76929.669
Median Absolute Deviation (MAD)45635.03
Skewness-0.15278862
Sum4.9119594 × 108
Variance3.8803099 × 109
MonotonicityNot monotonic
2025-11-19T22:17:34.926441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02283
28.5%
106234.021
 
< 0.1%
62389.031
 
< 0.1%
100421.11
 
< 0.1%
133446.341
 
< 0.1%
87271.411
 
< 0.1%
52192.081
 
< 0.1%
98548.621
 
< 0.1%
121376.151
 
< 0.1%
55053.621
 
< 0.1%
Other values (4093)4093
51.2%
(Missing)1615
 
20.2%
ValueCountFrequency (%)
02283
28.5%
3768.691
 
< 0.1%
12459.191
 
< 0.1%
14262.81
 
< 0.1%
16893.591
 
< 0.1%
27288.431
 
< 0.1%
28082.951
 
< 0.1%
28649.641
 
< 0.1%
29602.081
 
< 0.1%
33563.951
 
< 0.1%
ValueCountFrequency (%)
250898.091
< 0.1%
238387.561
< 0.1%
216109.881
< 0.1%
212778.21
< 0.1%
212692.971
< 0.1%
210433.081
< 0.1%
209490.211
< 0.1%
207034.961
< 0.1%
206868.781
< 0.1%
206329.651
< 0.1%

NumOfProducts
Categorical

Missing 

Distinct4
Distinct (%)0.1%
Missing976
Missing (%)12.2%
Memory size410.2 KiB
1.0
3484 
2.0
3323 
3.0
 
183
4.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21072
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.03484
43.5%
2.03323
41.5%
3.0183
 
2.3%
4.034
 
0.4%
(Missing)976
 
12.2%

Length

2025-11-19T22:17:35.092638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T22:17:35.212245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.03484
49.6%
2.03323
47.3%
3.0183
 
2.6%
4.034
 
0.5%

Most occurring characters

ValueCountFrequency (%)
.7024
33.3%
07024
33.3%
13484
16.5%
23323
15.8%
3183
 
0.9%
434
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)21072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.7024
33.3%
07024
33.3%
13484
16.5%
23323
15.8%
3183
 
0.9%
434
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.7024
33.3%
07024
33.3%
13484
16.5%
23323
15.8%
3183
 
0.9%
434
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.7024
33.3%
07024
33.3%
13484
16.5%
23323
15.8%
3183
 
0.9%
434
 
0.2%

HasCrCard
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing375
Missing (%)4.7%
Memory size407.8 KiB
1.0
5388 
0.0
2237 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22875
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.05388
67.3%
0.02237
28.0%
(Missing)375
 
4.7%

Length

2025-11-19T22:17:35.334943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T22:17:35.412502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.05388
70.7%
0.02237
29.3%

Most occurring characters

ValueCountFrequency (%)
09862
43.1%
.7625
33.3%
15388
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)22875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09862
43.1%
.7625
33.3%
15388
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09862
43.1%
.7625
33.3%
15388
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09862
43.1%
.7625
33.3%
15388
23.6%

IsActiveMember
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
1
4108 
0
3892 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Length

2025-11-19T22:17:35.522744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T22:17:35.627601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Most occurring characters

ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14108
51.3%
03892
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14108
51.3%
03892
48.6%

EstimatedSalary
Real number (ℝ)

Missing 

Distinct7167
Distinct (%)> 99.9%
Missing832
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean100057.17
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-11-19T22:17:35.781596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9817.0005
Q151545.353
median100129.08
Q3149216.32
95-th percentile189984.9
Maximum199992.48
Range199980.9
Interquartile range (IQR)97670.967

Descriptive statistics

Standard deviation57441.733
Coefficient of variation (CV)0.57408915
Kurtosis-1.1790461
Mean100057.17
Median Absolute Deviation (MAD)48801.565
Skewness0.002925442
Sum7.1720976 × 108
Variance3.2995527 × 109
MonotonicityNot monotonic
2025-11-19T22:17:36.005970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.922
 
< 0.1%
129956.131
 
< 0.1%
46172.471
 
< 0.1%
57553.981
 
< 0.1%
125979.361
 
< 0.1%
116124.281
 
< 0.1%
121440.81
 
< 0.1%
110932.241
 
< 0.1%
130686.591
 
< 0.1%
146371.721
 
< 0.1%
Other values (7157)7157
89.5%
(Missing)832
 
10.4%
ValueCountFrequency (%)
11.581
< 0.1%
90.071
< 0.1%
91.751
< 0.1%
96.271
< 0.1%
142.811
< 0.1%
178.191
< 0.1%
236.451
< 0.1%
247.361
< 0.1%
287.991
< 0.1%
332.811
< 0.1%
ValueCountFrequency (%)
199992.481
< 0.1%
199970.741
< 0.1%
199909.321
< 0.1%
199862.751
< 0.1%
199857.471
< 0.1%
199841.321
< 0.1%
199693.841
< 0.1%
199674.831
< 0.1%
199661.51
< 0.1%
199644.21
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
0
6370 
1
1630 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Length

2025-11-19T22:17:36.205929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T22:17:36.310866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Most occurring characters

ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06370
79.6%
11630
 
20.4%

Interactions

2025-11-19T22:17:26.615171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:18.903148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:22.896853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:23.786149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:24.750106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:25.653643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:26.767354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:20.235918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:23.057111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:23.938029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:24.891103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:25.804465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:26.905902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:20.866263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:23.190761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:24.107764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:25.056480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:25.954977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:27.072816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:21.517417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:23.330365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:24.255872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:25.212061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:26.173976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:27.232911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:22.525673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:23.480729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:24.414089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:25.353706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:26.324769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:27.368606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:22.684298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:23.629813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:24.573695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:25.505020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-19T22:17:26.455621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-19T22:17:36.421556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBalanceCreditScoreCustomerIdEstimatedSalaryExitedGenderGeographyHasCrCardIsActiveMemberNumOfProductsTenure
Age1.0000.026-0.0070.008-0.0010.3730.0200.0470.0180.1450.097-0.010
Balance0.0261.000-0.005-0.0150.0190.1430.0000.3240.0440.0000.235-0.004
CreditScore-0.007-0.0051.0000.018-0.0040.0810.0130.0030.0000.0490.044-0.002
CustomerId0.008-0.0150.0181.0000.0080.0320.0000.0100.0000.0170.000-0.025
EstimatedSalary-0.0010.019-0.0040.0081.0000.0000.0160.0080.0210.0170.0130.008
Exited0.3730.1430.0810.0320.0001.0000.1050.1730.0030.1510.3870.032
Gender0.0200.0000.0130.0000.0160.1051.0000.0160.0000.0250.0430.009
Geography0.0470.3240.0030.0100.0080.1730.0161.0000.0140.0030.0610.023
HasCrCard0.0180.0440.0000.0000.0210.0030.0000.0141.0000.0000.0000.039
IsActiveMember0.1450.0000.0490.0170.0170.1510.0250.0030.0001.0000.0400.027
NumOfProducts0.0970.2350.0440.0000.0130.3870.0430.0610.0000.0401.0000.035
Tenure-0.010-0.004-0.002-0.0250.0080.0320.0090.0230.0390.0270.0351.000

Missing values

2025-11-19T22:17:28.682682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-19T22:17:29.075228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-19T22:17:29.396069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
015759244Boone687.0GermanyMale44895368.142.01.011787.850
115725997She660.0FranceFemale356100768.771.01.0019199.610
215724296Kerr684.0SpainMale412119782.722.00.00120284.670
315636820Loggia725.0GermanyMale408104149.661.01.0062027.900
415744529Chiekwugo510.0FranceMale6380.002.01.01115291.860
515763907Watts820.0FranceFemale391104614.291.01.0061538.431
615671800Robinson688.0FranceMale208137624.402.01.01197582.790
715567383Slone678.0GermanyFemale44298009.132.00.0131384.860
815777179EllisNaNFranceMale359NaN2.00.01NaN0
915650391NaN633.0FranceFemale297169988.351.01.004272.000
CustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
799015775750Yao686.0FranceMale379134560.621.01.0027596.390
799115735878Law850.0GermanyFemale4710134381.52NaN0.0026812.891
799215737509Morrison850.0SpainMale348NaN1.00.00NaN0
799315782089MullenNaNFranceMale336NaN1.01.0058458.260
799415663921Pisani429.0FranceMale6070.002.01.01163691.480
799515628303Thurgood738.0SpainMale3530.001.01.0115650.730
799615699225Pirozzi757.0FranceMale4600.002.01.0037460.050
799715612525PrestonNaNFranceFemale571NaN1.00.00131372.381
799815724321Baresi516.0GermanyFemale479128298.741.00.00149614.171
799915578761CunninghamNaNSpainFemale426129634.25NaN1.01177683.021